Overview

Dataset statistics

Number of variables36
Number of observations10000
Missing cells204038
Missing cells (%)56.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory288.0 B

Variable types

Numeric9
Categorical10
Unsupported17

Alerts

J has constant value "0"Constant
UPN has a high cardinality: 8597 distinct valuesHigh cardinality
EntryDate has a high cardinality: 390 distinct valuesHigh cardinality
/ is highly overall correlated with \High correlation
\ is highly overall correlated with /High correlation
EnrolStatus is highly imbalanced (79.8%)Imbalance
P is highly imbalanced (53.3%)Imbalance
M is highly imbalanced (76.8%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
C has 9114 (91.1%) missing valuesMissing
E has 10000 (100.0%) missing valuesMissing
H has 10000 (100.0%) missing valuesMissing
I has 6710 (67.1%) missing valuesMissing
M has 8752 (87.5%) missing valuesMissing
R has 10000 (100.0%) missing valuesMissing
S has 10000 (100.0%) missing valuesMissing
T has 10000 (100.0%) missing valuesMissing
G has 10000 (100.0%) missing valuesMissing
N has 10000 (100.0%) missing valuesMissing
O has 9462 (94.6%) missing valuesMissing
U has 10000 (100.0%) missing valuesMissing
D has 10000 (100.0%) missing valuesMissing
X has 10000 (100.0%) missing valuesMissing
Y has 10000 (100.0%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
E is an unsupported type, check if it needs cleaning or further analysisUnsupported
H is an unsupported type, check if it needs cleaning or further analysisUnsupported
R is an unsupported type, check if it needs cleaning or further analysisUnsupported
S is an unsupported type, check if it needs cleaning or further analysisUnsupported
T is an unsupported type, check if it needs cleaning or further analysisUnsupported
G is an unsupported type, check if it needs cleaning or further analysisUnsupported
N is an unsupported type, check if it needs cleaning or further analysisUnsupported
U is an unsupported type, check if it needs cleaning or further analysisUnsupported
D is an unsupported type, check if it needs cleaning or further analysisUnsupported
X is an unsupported type, check if it needs cleaning or further analysisUnsupported
Y is an unsupported type, check if it needs cleaning or further analysisUnsupported
B has 1684 (16.8%) zerosZeros
L has 2140 (21.4%) zerosZeros

Reproduction

Analysis started2023-06-26 14:03:18.219573
Analysis finished2023-06-26 14:03:32.945200
Duration14.73 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9636
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191572
Minimum57574
Maximum294729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:33.038228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57574
5-th percentile132000.35
Q1167193.75
median191318
Q3216307.25
95-th percentile250422.6
Maximum294729
Range237155
Interquartile range (IQR)49113.5

Descriptive statistics

Standard deviation36084.513
Coefficient of variation (CV)0.18836006
Kurtosis-0.1296928
Mean191572
Median Absolute Deviation (MAD)24627.5
Skewness-0.042165821
Sum1.91572 × 109
Variance1.3020921 × 109
MonotonicityNot monotonic
2023-06-26T15:03:33.188544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202221 5
 
0.1%
186196 3
 
< 0.1%
224286 3
 
< 0.1%
190366 3
 
< 0.1%
144665 3
 
< 0.1%
184543 3
 
< 0.1%
180908 3
 
< 0.1%
188297 3
 
< 0.1%
175223 3
 
< 0.1%
177309 3
 
< 0.1%
Other values (9626) 9968
99.7%
ValueCountFrequency (%)
57574 1
< 0.1%
63840 1
< 0.1%
64659 1
< 0.1%
65629 1
< 0.1%
69212 1
< 0.1%
70759 1
< 0.1%
75394 1
< 0.1%
76381 1
< 0.1%
78698 1
< 0.1%
78833 1
< 0.1%
ValueCountFrequency (%)
294729 1
< 0.1%
293916 1
< 0.1%
292406 1
< 0.1%
292100 1
< 0.1%
292093 1
< 0.1%
291882 1
< 0.1%
291697 1
< 0.1%
291291 1
< 0.1%
291287 1
< 0.1%
291054 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8597
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
84d819e9-17fc-4baf-ac67-67b8123a71c1
 
4
7617e160-3eef-4b90-8260-402029a25c34
 
4
ac525a7d-3931-4f11-85cc-1a013d76cff3
 
4
f71f2e03-5a3f-44f2-9ff7-8de658031a71
 
4
f9802d4b-52e9-4e4a-aa53-62c617a312cd
 
4
Other values (8592)
9980 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7343 ?
Unique (%)73.4%

Sample

1st rowb4085463-6a46-450e-9ad8-dec00705b636
2nd rowd39f2449-640f-4a48-9163-3c9826b62c66
3rd row13ecf709-e032-4243-91d2-dd62e5e2951e
4th row1fc6d0b3-27b4-4c62-8fb0-f7d11c8dcfb8
5th row77449e7e-ce64-4576-a67e-49b039eab6d7

Common Values

ValueCountFrequency (%)
84d819e9-17fc-4baf-ac67-67b8123a71c1 4
 
< 0.1%
7617e160-3eef-4b90-8260-402029a25c34 4
 
< 0.1%
ac525a7d-3931-4f11-85cc-1a013d76cff3 4
 
< 0.1%
f71f2e03-5a3f-44f2-9ff7-8de658031a71 4
 
< 0.1%
f9802d4b-52e9-4e4a-aa53-62c617a312cd 4
 
< 0.1%
18c36a4b-c3cb-4921-b36c-2a19d772faf4 4
 
< 0.1%
5e3ad88a-01a4-406b-89cc-4ed91fa89c7b 4
 
< 0.1%
a606b312-69dd-46e6-bc79-d4da94bf55c7 4
 
< 0.1%
4a96e093-78d5-4559-8738-dd69b6c8c2c0 4
 
< 0.1%
a0ef097a-7d2c-4b47-9375-846f31ac344e 4
 
< 0.1%
Other values (8587) 9960
99.6%

Length

2023-06-26T15:03:33.326606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
84d819e9-17fc-4baf-ac67-67b8123a71c1 4
 
< 0.1%
a606b312-69dd-46e6-bc79-d4da94bf55c7 4
 
< 0.1%
d8c8e81f-b6de-4ec4-a43a-efc1456b8919 4
 
< 0.1%
d430c47f-1968-4c0b-86c6-0117a1d01525 4
 
< 0.1%
d67ceb16-5634-4c58-a591-03210d594a8c 4
 
< 0.1%
a0ef097a-7d2c-4b47-9375-846f31ac344e 4
 
< 0.1%
4a96e093-78d5-4559-8738-dd69b6c8c2c0 4
 
< 0.1%
7617e160-3eef-4b90-8260-402029a25c34 4
 
< 0.1%
5e3ad88a-01a4-406b-89cc-4ed91fa89c7b 4
 
< 0.1%
f9802d4b-52e9-4e4a-aa53-62c617a312cd 4
 
< 0.1%
Other values (8587) 9960
99.6%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 29029
 
8.1%
8 21569
 
6.0%
b 21193
 
5.9%
9 21161
 
5.9%
a 20808
 
5.8%
e 19053
 
5.3%
6 19002
 
5.3%
1 18954
 
5.3%
7 18920
 
5.3%
Other values (7) 130311
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203242
56.5%
Lowercase Letter 116758
32.4%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 29029
14.3%
8 21569
10.6%
9 21161
10.4%
6 19002
9.3%
1 18954
9.3%
7 18920
9.3%
3 18811
9.3%
5 18792
9.2%
0 18653
9.2%
2 18351
9.0%
Lowercase Letter
ValueCountFrequency (%)
b 21193
18.2%
a 20808
17.8%
e 19053
16.3%
d 18664
16.0%
f 18657
16.0%
c 18383
15.7%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 243242
67.6%
Latin 116758
32.4%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.4%
4 29029
11.9%
8 21569
8.9%
9 21161
8.7%
6 19002
7.8%
1 18954
7.8%
7 18920
7.8%
3 18811
7.7%
5 18792
7.7%
0 18653
7.7%
Latin
ValueCountFrequency (%)
b 21193
18.2%
a 20808
17.8%
e 19053
16.3%
d 18664
16.0%
f 18657
16.0%
c 18383
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 29029
 
8.1%
8 21569
 
6.0%
b 21193
 
5.9%
9 21161
 
5.9%
a 20808
 
5.8%
e 19053
 
5.3%
6 19002
 
5.3%
1 18954
 
5.3%
7 18920
 
5.3%
Other values (7) 130311
36.2%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
M
5134 
F
4866 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 5134
51.3%
F 4866
48.7%

Length

2023-06-26T15:03:33.436455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:33.547078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 5134
51.3%
f 4866
48.7%

Most occurring characters

ValueCountFrequency (%)
M 5134
51.3%
F 4866
48.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 5134
51.3%
F 4866
48.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 5134
51.3%
F 4866
48.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 5134
51.3%
F 4866
48.7%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
9205 
Leaver
 
792
M
 
2
S
 
1

Length

Max length6
Median length1
Mean length1.396
Min length1

Characters and Unicode

Total characters13960
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 9205
92.0%
Leaver 792
 
7.9%
M 2
 
< 0.1%
S 1
 
< 0.1%

Length

2023-06-26T15:03:33.651388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:33.773172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 9205
92.0%
leaver 792
 
7.9%
m 2
 
< 0.1%
s 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 9205
65.9%
e 1584
 
11.3%
L 792
 
5.7%
a 792
 
5.7%
v 792
 
5.7%
r 792
 
5.7%
M 2
 
< 0.1%
S 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
71.6%
Lowercase Letter 3960
 
28.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 9205
92.0%
L 792
 
7.9%
M 2
 
< 0.1%
S 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1584
40.0%
a 792
20.0%
v 792
20.0%
r 792
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13960
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 9205
65.9%
e 1584
 
11.3%
L 792
 
5.7%
a 792
 
5.7%
v 792
 
5.7%
r 792
 
5.7%
M 2
 
< 0.1%
S 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 9205
65.9%
e 1584
 
11.3%
L 792
 
5.7%
a 792
 
5.7%
v 792
 
5.7%
r 792
 
5.7%
M 2
 
< 0.1%
S 1
 
< 0.1%

EntryDate
Categorical

Distinct390
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2019-09-04 00:00:00
1165 
2020-09-03 00:00:00
1138 
2016-09-05 00:00:00
1020 
2017-09-06 00:00:00
868 
2018-09-07 00:00:00
637 
Other values (385)
5172 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)1.9%

Sample

1st row2018-09-03 00:00:00
2nd row2020-11-27 00:00:00
3rd row2018-09-04 00:00:00
4th row2019-09-04 00:00:00
5th row2017-09-06 00:00:00

Common Values

ValueCountFrequency (%)
2019-09-04 00:00:00 1165
 
11.7%
2020-09-03 00:00:00 1138
 
11.4%
2016-09-05 00:00:00 1020
 
10.2%
2017-09-06 00:00:00 868
 
8.7%
2018-09-07 00:00:00 637
 
6.4%
2018-09-06 00:00:00 505
 
5.1%
2016-09-01 00:00:00 473
 
4.7%
2018-09-05 00:00:00 471
 
4.7%
2020-09-02 00:00:00 426
 
4.3%
2020-09-01 00:00:00 366
 
3.7%
Other values (380) 2931
29.3%

Length

2023-06-26T15:03:33.875382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 10000
50.0%
2019-09-04 1165
 
5.8%
2020-09-03 1138
 
5.7%
2016-09-05 1020
 
5.1%
2017-09-06 868
 
4.3%
2018-09-07 637
 
3.2%
2018-09-06 505
 
2.5%
2016-09-01 473
 
2.4%
2018-09-05 471
 
2.4%
2020-09-02 426
 
2.1%
Other values (381) 3297
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 91630
48.2%
- 20000
 
10.5%
: 20000
 
10.5%
2 13608
 
7.2%
9 11480
 
6.0%
10000
 
5.3%
1 9767
 
5.1%
6 3121
 
1.6%
8 2429
 
1.3%
7 2330
 
1.2%
Other values (3) 5635
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91630
65.5%
2 13608
 
9.7%
9 11480
 
8.2%
1 9767
 
7.0%
6 3121
 
2.2%
8 2429
 
1.7%
7 2330
 
1.7%
5 1935
 
1.4%
4 1901
 
1.4%
3 1799
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91630
48.2%
- 20000
 
10.5%
: 20000
 
10.5%
2 13608
 
7.2%
9 11480
 
6.0%
10000
 
5.3%
1 9767
 
5.1%
6 3121
 
1.6%
8 2429
 
1.3%
7 2330
 
1.2%
Other values (3) 5635
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91630
48.2%
- 20000
 
10.5%
: 20000
 
10.5%
2 13608
 
7.2%
9 11480
 
6.0%
10000
 
5.3%
1 9767
 
5.1%
6 3121
 
1.6%
8 2429
 
1.3%
7 2330
 
1.2%
Other values (3) 5635
 
3.0%

NCyearActual
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
7
2074 
9
2048 
8
1912 
11
1851 
10
1753 
Other values (4)
362 

Length

Max length6
Median length1
Mean length1.489
Min length1

Characters and Unicode

Total characters14890
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row9
3rd row9
4th row7
5th row8

Common Values

ValueCountFrequency (%)
7 2074
20.7%
9 2048
20.5%
8 1912
19.1%
11 1851
18.5%
10 1753
17.5%
Leaver 231
 
2.3%
12 68
 
0.7%
13 36
 
0.4%
14 27
 
0.3%

Length

2023-06-26T15:03:33.998127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:34.139427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
7 2074
20.7%
9 2048
20.5%
8 1912
19.1%
11 1851
18.5%
10 1753
17.5%
leaver 231
 
2.3%
12 68
 
0.7%
13 36
 
0.4%
14 27
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 5586
37.5%
7 2074
 
13.9%
9 2048
 
13.8%
8 1912
 
12.8%
0 1753
 
11.8%
e 462
 
3.1%
L 231
 
1.6%
a 231
 
1.6%
v 231
 
1.6%
r 231
 
1.6%
Other values (3) 131
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13504
90.7%
Lowercase Letter 1155
 
7.8%
Uppercase Letter 231
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5586
41.4%
7 2074
 
15.4%
9 2048
 
15.2%
8 1912
 
14.2%
0 1753
 
13.0%
2 68
 
0.5%
3 36
 
0.3%
4 27
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 462
40.0%
a 231
20.0%
v 231
20.0%
r 231
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13504
90.7%
Latin 1386
 
9.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5586
41.4%
7 2074
 
15.4%
9 2048
 
15.2%
8 1912
 
14.2%
0 1753
 
13.0%
2 68
 
0.5%
3 36
 
0.3%
4 27
 
0.2%
Latin
ValueCountFrequency (%)
e 462
33.3%
L 231
16.7%
a 231
16.7%
v 231
16.7%
r 231
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5586
37.5%
7 2074
 
13.9%
9 2048
 
13.8%
8 1912
 
12.8%
0 1753
 
11.8%
e 462
 
3.1%
L 231
 
1.6%
a 231
 
1.6%
v 231
 
1.6%
r 231
 
1.6%
Other values (3) 131
 
0.9%

TermlySessionsPossible
Real number (ℝ)

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.65
Minimum0
Maximum102
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:34.321158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q127
median38
Q350
95-th percentile67
Maximum102
Range102
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.697454
Coefficient of variation (CV)0.43201693
Kurtosis-0.25495537
Mean38.65
Median Absolute Deviation (MAD)12
Skewness0.1744777
Sum386500
Variance278.80498
MonotonicityNot monotonic
2023-06-26T15:03:34.462519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 251
 
2.5%
39 235
 
2.4%
35 232
 
2.3%
45 230
 
2.3%
34 228
 
2.3%
38 228
 
2.3%
30 225
 
2.2%
41 225
 
2.2%
43 223
 
2.2%
32 223
 
2.2%
Other values (87) 7700
77.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 25
0.2%
2 32
0.3%
3 31
0.3%
4 28
0.3%
5 40
0.4%
6 43
0.4%
7 41
0.4%
8 53
0.5%
9 54
0.5%
ValueCountFrequency (%)
102 2
 
< 0.1%
98 1
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
92 2
 
< 0.1%
90 6
0.1%
89 2
 
< 0.1%
88 7
0.1%
87 4
< 0.1%

/
Real number (ℝ)

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.7583
Minimum0
Maximum44
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:34.606908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median17
Q321
95-th percentile28
Maximum44
Range44
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.794343
Coefficient of variation (CV)0.40543152
Kurtosis-0.22895436
Mean16.7583
Median Absolute Deviation (MAD)5
Skewness0.10607791
Sum167583
Variance46.163097
MonotonicityNot monotonic
2023-06-26T15:03:34.740226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16 606
 
6.1%
15 573
 
5.7%
17 569
 
5.7%
18 568
 
5.7%
14 542
 
5.4%
20 536
 
5.4%
19 527
 
5.3%
13 485
 
4.9%
21 465
 
4.7%
12 449
 
4.5%
Other values (33) 4680
46.8%
ValueCountFrequency (%)
0 23
 
0.2%
1 47
 
0.5%
2 65
 
0.7%
3 86
 
0.9%
4 122
 
1.2%
5 146
1.5%
6 168
1.7%
7 240
2.4%
8 288
2.9%
9 319
3.2%
ValueCountFrequency (%)
44 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 4
 
< 0.1%
38 3
 
< 0.1%
37 8
 
0.1%
36 15
 
0.1%
35 9
 
0.1%
34 20
0.2%
33 43
0.4%

\
Real number (ℝ)

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.0022
Minimum0
Maximum45
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:34.880217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median17
Q321
95-th percentile28
Maximum45
Range45
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.6341689
Coefficient of variation (CV)0.39019473
Kurtosis-0.20618728
Mean17.0022
Median Absolute Deviation (MAD)5
Skewness0.090904569
Sum170022
Variance44.012196
MonotonicityNot monotonic
2023-06-26T15:03:35.009421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
18 606
 
6.1%
17 603
 
6.0%
15 588
 
5.9%
16 559
 
5.6%
19 557
 
5.6%
20 518
 
5.2%
21 516
 
5.2%
13 511
 
5.1%
14 509
 
5.1%
12 477
 
4.8%
Other values (33) 4556
45.6%
ValueCountFrequency (%)
0 20
 
0.2%
1 32
 
0.3%
2 60
 
0.6%
3 63
 
0.6%
4 100
 
1.0%
5 128
1.3%
6 170
1.7%
7 217
2.2%
8 254
2.5%
9 278
2.8%
ValueCountFrequency (%)
45 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 3
 
< 0.1%
37 9
 
0.1%
36 3
 
< 0.1%
35 21
0.2%
34 20
0.2%
33 40
0.4%

B
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8808
Minimum0
Maximum10
Zeros1684
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:35.129384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4823667
Coefficient of variation (CV)0.78815755
Kurtosis0.70586255
Mean1.8808
Median Absolute Deviation (MAD)1
Skewness0.88527712
Sum18808
Variance2.1974111
MonotonicityNot monotonic
2023-06-26T15:03:35.238278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 3031
30.3%
2 2370
23.7%
0 1684
16.8%
3 1510
15.1%
4 804
 
8.0%
5 396
 
4.0%
6 142
 
1.4%
7 50
 
0.5%
8 9
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 1684
16.8%
1 3031
30.3%
2 2370
23.7%
3 1510
15.1%
4 804
 
8.0%
5 396
 
4.0%
6 142
 
1.4%
7 50
 
0.5%
8 9
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 3
 
< 0.1%
8 9
 
0.1%
7 50
 
0.5%
6 142
 
1.4%
5 396
 
4.0%
4 804
 
8.0%
3 1510
15.1%
2 2370
23.7%
1 3031
30.3%

J
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-06-26T15:03:35.354125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:35.465552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10000
100.0%

L
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4459
Minimum0
Maximum7
Zeros2140
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:35.542231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1651648
Coefficient of variation (CV)0.8058405
Kurtosis0.73425167
Mean1.4459
Median Absolute Deviation (MAD)1
Skewness0.83924483
Sum14459
Variance1.357609
MonotonicityNot monotonic
2023-06-26T15:03:35.662275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3723
37.2%
2 2399
24.0%
0 2140
21.4%
3 1192
 
11.9%
4 409
 
4.1%
5 103
 
1.0%
6 27
 
0.3%
7 7
 
0.1%
ValueCountFrequency (%)
0 2140
21.4%
1 3723
37.2%
2 2399
24.0%
3 1192
 
11.9%
4 409
 
4.1%
5 103
 
1.0%
6 27
 
0.3%
7 7
 
0.1%
ValueCountFrequency (%)
7 7
 
0.1%
6 27
 
0.3%
5 103
 
1.0%
4 409
 
4.1%
3 1192
 
11.9%
2 2399
24.0%
1 3723
37.2%
0 2140
21.4%

P
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7913 
1
2086 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7913
79.1%
1 2086
 
20.9%
2 1
 
< 0.1%

Length

2023-06-26T15:03:36.035582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:36.163765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7913
79.1%
1 2086
 
20.9%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7913
79.1%
1 2086
 
20.9%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7913
79.1%
1 2086
 
20.9%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7913
79.1%
1 2086
 
20.9%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7913
79.1%
1 2086
 
20.9%
2 1
 
< 0.1%

V
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
6934 
1
3046 
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 6934
69.3%
1 3046
30.5%
2 20
 
0.2%

Length

2023-06-26T15:03:36.287512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:36.420854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6934
69.3%
1 3046
30.5%
2 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 6934
69.3%
1 3046
30.5%
2 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6934
69.3%
1 3046
30.5%
2 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6934
69.3%
1 3046
30.5%
2 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6934
69.3%
1 3046
30.5%
2 20
 
0.2%

W
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
4835 
0
4166 
2
941 
3
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 4835
48.4%
0 4166
41.7%
2 941
 
9.4%
3 58
 
0.6%

Length

2023-06-26T15:03:36.546467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:36.680825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4835
48.4%
0 4166
41.7%
2 941
 
9.4%
3 58
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 4835
48.4%
0 4166
41.7%
2 941
 
9.4%
3 58
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4835
48.4%
0 4166
41.7%
2 941
 
9.4%
3 58
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4835
48.4%
0 4166
41.7%
2 941
 
9.4%
3 58
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4835
48.4%
0 4166
41.7%
2 941
 
9.4%
3 58
 
0.6%

C
Real number (ℝ)

Distinct48
Distinct (%)5.4%
Missing9114
Missing (%)91.1%
Infinite0
Infinite (%)0.0%
Mean20.967269
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:36.809282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q115
median21
Q326
95-th percentile35
Maximum49
Range48
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.5060331
Coefficient of variation (CV)0.40568151
Kurtosis-0.13528564
Mean20.967269
Median Absolute Deviation (MAD)6
Skewness0.21122639
Sum18577
Variance72.3526
MonotonicityNot monotonic
2023-06-26T15:03:36.981523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
17 51
 
0.5%
21 48
 
0.5%
24 46
 
0.5%
16 42
 
0.4%
19 40
 
0.4%
23 39
 
0.4%
20 39
 
0.4%
22 38
 
0.4%
25 36
 
0.4%
14 32
 
0.3%
Other values (38) 475
 
4.8%
(Missing) 9114
91.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 5
 
0.1%
3 5
 
0.1%
4 7
 
0.1%
5 6
 
0.1%
6 14
0.1%
7 9
 
0.1%
8 14
0.1%
9 17
0.2%
10 25
0.2%
ValueCountFrequency (%)
49 1
 
< 0.1%
48 1
 
< 0.1%
46 1
 
< 0.1%
45 2
 
< 0.1%
44 2
 
< 0.1%
43 1
 
< 0.1%
42 2
 
< 0.1%
41 3
< 0.1%
40 3
< 0.1%
39 5
0.1%

E
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

H
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

I
Real number (ℝ)

Distinct14
Distinct (%)0.4%
Missing6710
Missing (%)67.1%
Infinite0
Infinite (%)0.0%
Mean3.575076
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:37.104281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1807887
Coefficient of variation (CV)0.60999787
Kurtosis1.1880316
Mean3.575076
Median Absolute Deviation (MAD)1
Skewness1.08088
Sum11762
Variance4.7558395
MonotonicityNot monotonic
2023-06-26T15:03:37.231351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 779
 
7.8%
3 581
 
5.8%
1 508
 
5.1%
4 483
 
4.8%
5 364
 
3.6%
6 225
 
2.2%
7 149
 
1.5%
8 97
 
1.0%
9 52
 
0.5%
10 29
 
0.3%
Other values (4) 23
 
0.2%
(Missing) 6710
67.1%
ValueCountFrequency (%)
1 508
5.1%
2 779
7.8%
3 581
5.8%
4 483
4.8%
5 364
3.6%
6 225
 
2.2%
7 149
 
1.5%
8 97
 
1.0%
9 52
 
0.5%
10 29
 
0.3%
ValueCountFrequency (%)
14 2
 
< 0.1%
13 6
 
0.1%
12 3
 
< 0.1%
11 12
 
0.1%
10 29
 
0.3%
9 52
 
0.5%
8 97
 
1.0%
7 149
1.5%
6 225
2.2%
5 364
3.6%

M
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)0.2%
Missing8752
Missing (%)87.5%
Memory size78.2 KiB
1.0
1162 
2.0
 
85
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3744
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1162
 
11.6%
2.0 85
 
0.9%
3.0 1
 
< 0.1%
(Missing) 8752
87.5%

Length

2023-06-26T15:03:37.367628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:03:37.496895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1162
93.1%
2.0 85
 
6.8%
3.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 1248
33.3%
0 1248
33.3%
1 1162
31.0%
2 85
 
2.3%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2496
66.7%
Other Punctuation 1248
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1248
50.0%
1 1162
46.6%
2 85
 
3.4%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3744
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1248
33.3%
0 1248
33.3%
1 1162
31.0%
2 85
 
2.3%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1248
33.3%
0 1248
33.3%
1 1162
31.0%
2 85
 
2.3%
3 1
 
< 0.1%

R
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

S
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

T
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

G
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

N
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

O
Real number (ℝ)

Distinct11
Distinct (%)2.0%
Missing9462
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean4.8736059
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:03:37.595566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8608008
Coefficient of variation (CV)0.38181191
Kurtosis-0.23785077
Mean4.8736059
Median Absolute Deviation (MAD)1
Skewness0.18338194
Sum2622
Variance3.4625795
MonotonicityNot monotonic
2023-06-26T15:03:37.724113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 114
 
1.1%
4 104
 
1.0%
6 93
 
0.9%
3 67
 
0.7%
7 57
 
0.6%
2 46
 
0.5%
8 27
 
0.3%
9 14
 
0.1%
1 13
 
0.1%
10 2
 
< 0.1%
(Missing) 9462
94.6%
ValueCountFrequency (%)
1 13
 
0.1%
2 46
0.5%
3 67
0.7%
4 104
1.0%
5 114
1.1%
6 93
0.9%
7 57
0.6%
8 27
 
0.3%
9 14
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 14
 
0.1%
8 27
 
0.3%
7 57
0.6%
6 93
0.9%
5 114
1.1%
4 104
1.0%
3 67
0.7%
2 46
0.5%

U
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

D
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

X
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Y
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Interactions

2023-06-26T15:03:30.358342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:19.757417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.151296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:22.530438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:23.693571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.808048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.947424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:27.405128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:28.838222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:30.512823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:19.920347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.287185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:22.653465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:23.832676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.933161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:26.122814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:27.587945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:29.018631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:30.658278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:20.082132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.429445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:22.769542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:23.950170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.065187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:26.287468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:27.738380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:29.177271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:30.807475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:20.233626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.556826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:22.880425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.070619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.176745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:26.424089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:27.945546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:29.355403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:31.216836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:20.388412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.688350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:22.996330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.178190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.294175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:26.566955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:28.113254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:29.513083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:31.390479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:20.536511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.823546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:23.124344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.302071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.412458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:26.721090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:28.279272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:29.684722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:31.533208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:20.689644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.970343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:23.297453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.423793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.537743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:26.882261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:28.435216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:29.892980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:31.674382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:20.860980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:22.107116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:23.425661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.567557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.672462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:27.060113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:28.583995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:30.048490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:31.782608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:21.020884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:22.248622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:23.568209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:24.687270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:25.798235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:27.237997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:28.711228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:30.210078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T15:03:37.906181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EstabTermlySessionsPossible/\BLCIOGenderEnrolStatusNCyearActualPVWM
Estab1.0000.2470.0120.005-0.114-0.0890.2230.013-0.0000.0290.0000.0870.0000.0830.0240.000
TermlySessionsPossible0.2471.0000.4830.4970.0370.0500.4890.0240.0170.0400.0000.0200.0000.1070.0110.000
/0.0120.4831.0000.955-0.129-0.057-0.052-0.116-0.1900.0200.0200.0220.0000.0900.0000.092
\0.0050.4970.9551.000-0.1390.043-0.045-0.098-0.1950.0210.0000.0140.0150.0690.0000.106
B-0.1140.037-0.129-0.1391.0000.0470.030-0.0730.0340.0000.0400.0230.1450.1030.1650.000
L-0.0890.050-0.0570.0430.0471.0000.009-0.017-0.1120.0190.0000.0120.0270.0200.0310.000
C0.2230.489-0.052-0.0450.0300.0091.0000.0050.1010.0000.0500.0210.0220.0000.0000.000
I0.0130.024-0.116-0.098-0.073-0.0170.0051.000-0.0910.0000.0000.0000.0160.0110.0290.000
O-0.0000.017-0.190-0.1950.034-0.1120.101-0.0911.0000.1190.0000.0000.0000.0000.0000.202
Gender0.0290.0400.0200.0210.0000.0190.0000.0000.1191.0000.0050.0000.0100.0000.0000.011
EnrolStatus0.0000.0000.0200.0000.0400.0000.0500.0000.0000.0051.0000.0000.0470.0340.0380.094
NCyearActual0.0870.0200.0220.0140.0230.0120.0210.0000.0000.0000.0001.0000.0330.0440.0500.000
P0.0000.0000.0000.0150.1450.0270.0220.0160.0000.0100.0470.0331.0000.1050.1910.000
V0.0830.1070.0900.0690.1030.0200.0000.0110.0000.0000.0340.0440.1051.0000.1100.000
W0.0240.0110.0000.0000.1650.0310.0000.0290.0000.0000.0380.0500.1910.1101.0000.000
M0.0000.0000.0920.1060.0000.0000.0000.0000.2020.0110.0940.0000.0000.0000.0001.000

Missing values

2023-06-26T15:03:32.036098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:03:32.572444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-26T15:03:32.861561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
0185405b4085463-6a46-450e-9ad8-dec00705b636NaNNaNNaNNaNNaNFNaNC2018-09-03 00:00:0010432121002100NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1222942d39f2449-640f-4a48-9163-3c9826b62c66NaNNaNNaNNaNNaNMNaNC2020-11-27 00:00:009531922202002NaNNaNNaN3.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
214755013ecf709-e032-4243-91d2-dd62e5e2951eNaNNaNNaNNaNNaNFNaNC2018-09-04 00:00:009512220103002NaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32581231fc6d0b3-27b4-4c62-8fb0-f7d11c8dcfb8NaNNaNNaNNaNNaNFNaNC2019-09-04 00:00:007502324002000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
413477477449e7e-ce64-4576-a67e-49b039eab6d7NaNNaNNaNNaNNaNMNaNC2017-09-06 00:00:0081498200010NaNNaNNaN6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5137223a62cc6bf-9544-4372-af46-75ae1d1d4c27NaNNaNNaNNaNNaNMNaNC2018-09-06 00:00:0073098401001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
613708659d3b31a-15cd-42f2-9004-d3559ee8201eNaNNaNNaNNaNNaNFNaNC2015-02-02 00:00:0011291515401100NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7167905d236b5da-7c0f-4cb8-9fa8-ad7b1faf82feNaNNaNNaNNaNNaNMNaNC2019-09-04 00:00:008563230100001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
822091283aa98d9-9621-4f0f-9d07-6b6db9b3f0ecNaNNaNNaNNaNNaNMNaNC2017-09-06 00:00:0010181717300100NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9154687e3f540d7-c931-4540-a455-3079ca8c18b1NaNNaNNaNNaNNaNFNaNC2019-01-23 00:00:0010371715301000NaNNaNNaN4.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
9990207439e5234336-e018-4b6b-b8eb-8d4f62cfc5ecNaNNaNNaNNaNNaNFNaNC2017-09-06 00:00:009502423202001NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
999117994154da22e4-35e2-44c8-8b0c-bdbe24f965e1NaNNaNNaNNaNNaNMNaNC2020-09-02 00:00:008542927101001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
999218295642ed18ba-82d8-4100-b056-62c7d0d1cbefNaNNaNNaNNaNNaNFNaNC2020-09-04 00:00:0010482021003000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
999315402328280060-6d12-44fb-9a57-62c6edb47e02NaNNaNNaNNaNNaNMNaNC2016-09-05 00:00:0010321113303001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.0NaNNaNNaNNaN
999418762052104a73-b423-4b82-a363-ca0ccb120872NaNNaNNaNNaNNaNMNaNC2018-09-06 00:00:007391515101000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99951779308545d8e6-755b-4bac-b43e-70259852d174NaNNaNNaNNaNNaNFNaNC2018-09-04 00:00:0011632925101011NaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9996194651ba1fc0f5-797a-4551-88da-b749a4c68fd3NaNNaNNaNNaNNaNFNaNC2019-09-05 00:00:008271312202001NaNNaNNaN6.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
999724299091968c84-f501-48dd-aad5-a57bbf89f6e6NaNNaNNaNNaNNaNFNaNC2016-09-01 00:00:007232019001000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9998174450616a22cf-51a3-401e-9c32-575beeb9b4a9NaNNaNNaNNaNNaNMNaNC2019-09-04 00:00:007452122001000NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99991148857b22baae-892d-4c07-9263-a97ecd6ec6f8NaNNaNNaNNaNNaNFNaNC2020-09-03 00:00:008201717000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN